In Linear regression we covered:
- Single Feature
- Multiple Feature
- Model Formulation and Setup
- Loss Function( How to solve?, Reformulation, python code)
- Solve Optimization Problem (Analytical Solution employing Calculus)
- Model Evaluation Techniques
- Polynomial Regression
- How to Handle Overfitting?
- Regularization (Ridge Regression and Lasso Regression)
- Gradient Descent Algorithm ( Formulation, Algorithm, python code, Types of GD)
- Linear Regression Implementation in Python
- Linear Regression Implementation using sklearn
- Project: Medical Insurance Cost Prediction
- Interview Questions